ORCID Profile
0000-0003-0201-5348
Current Organisation
Queensland University of Technology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Biological Mathematics | Optimisation | Applied Mathematics | Operations Research | Other Biological Sciences | Environmental Management | Climate Change Processes | Global Change Biology
Effects of Climate Change and Variability on Antarctic and Sub-Antarctic Environments (excl. Social Impacts) | Climate Change Adaptation Measures | Ecosystem Assessment and Management of Antarctic and Sub-Antarctic Environments | Environmental Policy, Legislation and Standards not elsewhere classified | Ecosystem Assessment and Management at Regional or Larger Scales | Expanding Knowledge in the Mathematical Sciences |
Publisher: Wiley
Date: 27-06-2021
DOI: 10.1002/RSE2.221
Abstract: Forest cover requires large scale and frequent monitoring as an indicator of bio ersity and progress towards United Nations and World Bank Sustainable Development Goal 15. Measuring change in forest cover over time is an essential task in order to track and preserve quality habitats for species around the world. Due to the prohibitive expense and impracticality of mass field data collection to monitor forest cover at regular intervals, satellite images are a key data source for monitoring forest cover globally. A challenge of working with satellite images is missing data due to clouds. Existing methods for interpolating the missing data based on past images, such as compositing, are effective for stable land cover but can be inaccurate for dynamic and substantially changing landscapes. Here we present an adaptation of our recent stochastic spatial random forest (SS‐RF) method, which combines observed data from a prior image and modelled estimates of the current image to produce interpolated land cover values and associated probabilities of those values. Results show our SS‐RF method accurately detected simulated land cover change under both clear felling (0.83 average overall accuracy) and tree thinning (0.85 average overall accuracy). Our method detected forest cover change substantially more accurately than compositing, offering 39% and 12% increases in average overall accuracy for clear felling and tree thinning simulations respectively. However, when natural fluctuation occurs and there is minimal change in land cover, compositing has equivalent or more accurate performance than our method. Overall we find that our SS‐RF method produces accurate estimates under a range of simulated forest clearing scenarios and has a more accurate and robust performance than compositing when modelling noticeably changing landscapes.
Publisher: Wiley
Date: 19-07-2013
Publisher: Wiley
Date: 29-11-2017
DOI: 10.1111/ACV.12319
Publisher: ACM
Date: 20-06-2018
Publisher: MDPI AG
Date: 31-07-2019
DOI: 10.3390/RS11151796
Abstract: Sustainable Development Goals (SDGs) are a set of priorities the United Nations and World Bank have set for countries to reach in order to improve quality of life and environment globally by 2030. Free satellite images have been identified as a key resource that can be used to produce official statistics and analysis to measure progress towards SDGs, especially those that are concerned with the physical environment, such as forest, water, and crops. Satellite images can often be unusable due to missing data from cloud cover, particularly in tropical areas where the deforestation rates are high. There are existing methods for filling in image gaps however, these are often computationally expensive in image classification or not effective at pixel scale. To address this, we use two machine learning methods—gradient boosted machine and random forest algorithms—to classify the observed and simulated ‘missing’ pixels in satellite images as either grassland or woodland. We also predict a continuous biophysical variable, Foliage Projective Cover (FPC), which was derived from satellite images, and perform accurate binary classification and prediction using only the latitude and longitude of the pixels. We compare the performance of these methods against each other and inverse distance weighted interpolation, which is a well-established spatial interpolation method. We find both of the machine learning methods, particularly random forest, perform fast and accurate classifications of both observed and missing pixels, with up to 0.90 accuracy for the binary classification of pixels as grassland or woodland. The results show that the random forest method is more accurate than inverse distance weighted interpolation and gradient boosted machine for prediction of FPC for observed and missing data. Based on the case study results from a sub-tropical site in Australia, we show that our approach provides an efficient alternative for interpolating images and performing land cover classifications.
Publisher: Wiley
Date: 14-01-2016
Publisher: Wiley
Date: 21-02-2018
Publisher: Elsevier BV
Date: 11-2021
Publisher: MDPI AG
Date: 17-04-2023
Abstract: Dynamical properties of numerically approximated discrete systems may become inconsistent with those of the corresponding continuous-time system. We present a qualitative analysis of the dynamical properties of two-species Lotka-Volterra and Ricker-type predator-prey systems under discrete and continuous settings. By creating an arbitrary time discretisation, we obtain stability conditions that preserve the characteristics of continuous-time models and their numerically approximated systems. Here, we show that even small changes to some of the model parameters may alter the system dynamics unless an appropriate time discretisation is chosen to return similar dynamical behaviour to what is observed in the corresponding continuous-time system. We also found similar dynamical properties of the Ricker-type predator-prey systems under certain conditions. Our results demonstrate the need for preliminary analysis to identify which dynamical properties of approximated discretised systems agree or disagree with the corresponding continuous-time systems.
Publisher: Wiley
Date: 10-2014
DOI: 10.1890/13-1579.1
Abstract: Fences that exclude alien invasive species are used to reduce predation pressure on reintroduced threatened wildlife. Planning these continuously managed systems of reserves raises an important extension of the Single Large or Several Small (SLOSS) reserve planning framework: the added complexity of ongoing management. We investigate the long-term cost-efficiency of a single large or two small predator exclusion fences in the arid Australian context of reintroducing bilbies Macrotis lagotis, and we highlight the broader significance of our results with sensitivity analysis. A single fence more frequently results in a much larger net cost than two smaller fences. We find that the cost-efficiency of two fences is robust to strong demographic and environmental uncertainty, which can help managers to mitigate the risk of incurring high costs over the entire life of the project.
Publisher: Springer Science and Business Media LLC
Date: 31-07-2020
DOI: 10.1186/S40537-020-00331-8
Abstract: Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these images often have missing data in tropical regions due to climate driven persistent cloud cover. Remote sensing and statistical approaches to filling these missing data gaps exist and these can be highly accurate, but any interpolation method results are uncertain and these methods do not provide measures of this uncertainty. We present a new two-step spatial stochastic random forest (SS-RF) method that uses random forest algorithms to construct Beta distributions for interpolating missing data. This method has comparable performance with the traditional remote sensing compositing method, and additionally provides a probability for each interpolated data point. Our results show that the SS-RF method can accurately interpolate missing data and quantify uncertainty and its applicability to the challenge of monitoring forest using free and incomplete satellite imagery data. We propose that there is scope for our SS-RF method to be applied to other big data problems where a measurement of uncertainty is needed in addition to estimates.
Publisher: Public Library of Science (PLoS)
Date: 09-01-2019
Publisher: Elsevier BV
Date: 05-2022
Publisher: Wiley
Date: 10-01-2020
DOI: 10.1002/EAP.2057
Abstract: Understanding the trade-offs between bio ersity conservation and agricultural production has become a fundamental question in sustainability science. Substantial research has focused on how species' populations respond to agricultural intensification, with the goal to understand whether conservation policies that spatially separate agriculture and conservation or, alternatively, integrate the two are more beneficial. Spatial heterogeneity in both species abundance and agricultural productivity have been largely left out of this discussion, although these patterns are ubiquitous from local to global scales due to varying land capacity. Here, we address the question of how to align agricultural production and bio ersity conservation in heterogeneous landscapes. Using model simulations of species abundance and agricultural yields, we show that trade-offs between agricultural production and species' abundance can be reduced by minimizing the cost (in terms of species abundance) of agricultural production. We find that when species' abundance and agricultural yields vary across landscapes, the optimal strategy to minimize trade-offs is rarely pure land sparing or land sharing. Instead, landscapes that combine elements of both strategies are optimal. Additionally, we show how the reference population of a species is defined has important influences on optimization results. Our findings suggest that in the real world, understanding the impact of heterogeneous land capacity on bio ersity and agricultural production is crucial to designing multi-use landscapes that jointly maximize conservation and agricultural benefits.
Publisher: Public Library of Science (PLoS)
Date: 26-08-2020
Publisher: Elsevier BV
Date: 03-2018
DOI: 10.1016/J.JENVMAN.2017.12.026
Abstract: Food, energy, and water (FEW) systems are inexorably linked. Earth's changing climate and increasing competition for finite land resources are creating and lifying challenges at the FEW nexus. Managing FEW systems to mitigate these negative impacts and stresses is a pressing policy issue. The FEW interface is often managed as three independent systems, missing disruptive opportunities for streamlined integrated management. We contend that existing technologies can be reframed and emerging technologies can be harnessed for integrated FEW management, changing the way that each resource system operates within the broader system. We discuss solutions to three main challenges to integrating FEW system management: resolving spatiotemporal disconnections over multiple scales closing resource loops and creating actionable information. Sustainable resource management is critical for humanity, as well as for functioning trade systems and ecological health. Embracing integrated management in FEW systems would enable policy makers and managers to more efficiently and effectively secure critical resource systems in the face of global change.
Publisher: CSIRO Publishing
Date: 2021
DOI: 10.1071/PC20002
Abstract: Coral reefs are one of the ecosystems most sensitive to climate change. The recent loss and degradation of coral reef ecosystems is expected to continue even if global warming is limited to 1.5°C above pre-industrial levels. There is therefore an urgent need to develop new technologies and management approaches to coral reef conservation. Emerging technologies, however, bring new challenges for decision making as they are associated with novel risks and impacts on the reef system. We argue that accounting for multiple biophysical thresholds and identifying key decisions in the planning process are crucial to avoid perverse outcomes in coral reef conservation. We identify opportunities to apply an Adaptation Pathways (AP) framework combined with a ‘values, rules and knowledge’ approach to facilitate long-term and large-scale coral reef conservation by explicitly considering uncertainty with climate change impacts in the decision-making process. The AP process can be used to reconcile the erse values of stakeholders, scientific and cultural knowledge about the system, and the policy and regulatory context within which management must occur, in order to achieve robust long-term management.
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 06-2016
DOI: 10.1016/J.TPB.2016.02.001
Abstract: Increasing the colonization rate of metapopulations can improve persistence, but can also increase exposure to threats. To make good decisions, managers must understand whether increased colonization is beneficial or detrimental to metapopulation persistence. While a number of studies have examined interactions between metapopulations, colonization, and threats, they have assumed that threat dynamics respond linearly to changes in colonization. Here, we determined when to increase colonization while explicitly accounting for non-linear dependencies between a metapopulation and its threats. We developed patch occupancy metapopulation models for species susceptible to abiotic, generalist, and specialist threats and modeled the total derivative of the equilibrium proportion of patches occupied by each metapopulation with respect to the colonization rate. By using the total derivative, we developed a rule for determining when to increase metapopulation colonization. This rule was applied to a simulated metapopulation where the dynamics of each threat responded to increased colonization following a power function. Before modifying colonization, we show that managers must understand: (1) whether a metapopulation is susceptible to a threat (2) the type of threat acting on a metapopulation (3) which component of threat dynamics might depend on colonization, and (4) the likely response of a threat-dependent variable to changes in colonization. The sensitivity of management decisions to these interactions increases uncertainty in conservation planning decisions.
Publisher: Wiley
Date: 25-02-2021
DOI: 10.1111/GCB.15539
Abstract: Globally, collapse of ecosystems—potentially irreversible change to ecosystem structure, composition and function—imperils bio ersity, human health and well‐being. We examine the current state and recent trajectories of 19 ecosystems, spanning 58° of latitude across 7.7 M km 2 , from Australia's coral reefs to terrestrial Antarctica. Pressures from global climate change and regional human impacts, occurring as chronic ‘presses’ and/or acute ‘pulses’, drive ecosystem collapse. Ecosystem responses to 5–17 pressures were categorised as four collapse profiles—abrupt, smooth, stepped and fluctuating. The manifestation of widespread ecosystem collapse is a stark warning of the necessity to take action. We present a three‐step assessment and management framework (3As Pathway Awareness , Anticipation and Action ) to aid strategic and effective mitigation to alleviate further degradation to help secure our future.
Publisher: Public Library of Science (PLoS)
Date: 15-03-2023
DOI: 10.1371/JOURNAL.PONE.0282668
Abstract: Production of cultivated resources require additional planning that takes growth time into account. We formulate a mathematical programming model to determine the optimal location and sizing of growth facilities, impacted by resource survival rate as a function of its growth time. Our method informs strategic decisions regarding the number, location, and sizing of facilities, as well as operational decisions of optimal growth time for a cultivated resource in a facility to minimize total costs. We solve this facility location and sizing problem in the context of coral aquaculture for large-scale reef restoration using a two-stage algorithm and a linear mixed-integer solver. We assess growth time in a facility in terms of its impact on survival (post-deployment) considering growth quantity requirements and growth facility production constraints. We explore the sensitivity of optimal facility number, location, and sizing to changes in the geographic distribution of demand and cost parameters computationally. Results show that the relationship between growth time and survival is critical to optimizing operational decisions for grown resources. These results inform the value of data certainty to optimize the logistics of coral aquaculture production.
Publisher: Informa UK Limited
Date: 14-02-2021
Publisher: Wiley
Date: 21-02-2018
Publisher: Wiley
Date: 26-09-2018
Publisher: Wiley
Date: 27-01-2020
DOI: 10.1111/ELE.13465
Abstract: Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.
Publisher: Elsevier BV
Date: 03-2023
Publisher: Informa UK Limited
Date: 12-2021
Publisher: Elsevier BV
Date: 03-2022
DOI: 10.1016/J.TREE.2021.11.012
Abstract: Social-ecological networks (SENs) represent the complex relationships between ecological and social systems and are a useful tool for analyzing and managing ecosystem services. However, mainstreaming the application of SENs in ecosystem service research has been hindered by a lack of clarity about how to match research questions to ecosystem service conceptualizations in SEN (i.e., as nodes, links, attributes, or emergent properties). Building from different disciplines, we propose a typology to represent ecosystem service in SENs and identify opportunities and challenges of using SENs in ecosystem service research. Our typology provides guidance for this growing field to improve research design and increase the breadth of questions that can be addressed with SEN to understand human-nature interdependencies in a changing world.
Publisher: Wiley
Date: 03-03-2023
DOI: 10.1002/FES3.453
Abstract: Beef production drives a significant proportion of Australia's agricultural economy, representing AUD11 billion in agricultural production value annually. The profitability of cattle farms across seasons varies greatly and is particularly influenced by weather and associated uncertainty through pasture growth. Furthermore, producers must be careful in managing stocking rates to avoid overgrazing which has a significant environmental and economic impact. Predictive modelling of pasture and cattle liveweight can help producers to better manage this difficult balancing act. In this paper, we integrate a model for pasture growth and cattle weight gain to formulate predictions of liveweight production volume and pasture biomass. Monte‐Carlo simulation was used to understand the impact of uncertainty in model inputs relating to weather. Through such an approach, a distribution of possible pasture outcomes and beef production is formed which can be inspected to determine, for ex le expected production volume and risk of overgrazing. The methodology is demonstrated and validated through application to case study data sourced from Australian beef producers. Results demonstrate that it is possible to obtain reasonable predictions of liveweight gain and highlight the importance of considering the associated uncertainty. The accuracy of the proposed models has been examined by calculating statistical parameters MSE, MAE and R 2 . This approach can provide insight into the distribution of possible production outcomes given the long‐term rainfall outlook. The use of such predictive models and simulations by producers will lead to better‐informed planning of stocking rates, with benefits to productivity and sustainable utilisation of pasture.
Location: United States of America
Start Date: 02-2020
End Date: 12-2024
Amount: $427,082.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2021
End Date: 06-2030
Amount: $36,000,000.00
Funder: Australian Research Council
View Funded Activity